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1 LOGO Automatic Land Parcel Valuation to Support the Land and Buildings Tax Information System by Developing the Open Source Software Bambang Edhi Leksono, Yuliana Susilowati, Andriyan Bayu Suksmono Graduate Program for Land Administration, Bandung Institute of Technology. Labtek IX-C 3 rd floor, Jl. Ganesha 1, Bandung, 4132, Indonesia. Tel Fax AUTOMATIC LAND PARCEL VALUATION The purpose of Land Valuation: Provide a credible and realiable and cost-effective estimate of land value as of given point in time

2 BACKGROUND Uncertainty Multicolinearity Land Value Characteristic Variable Location ANN Method Non Linearity ANN Method MRA Method Problem Identification & Conceptual Framework LAND VALUE METHOD Multiple Regression Analysis (MRA) MRA is one of the most widely used method for land valuation models. MRA is a statistically based analysis that evaluates linear relationship between a dependent (response) variable and several independent (predictor variable), and extracts parameter estimates for independent variables used collectively to estimate value in a mathematical model. Artificial Neural Network (ANN) ANN is Computation method applies approach of pattern recognition to solve problem. ANN can calibrate models that consist of both linear and nonlinear term simultaneously.

3 RESEARCH QUESTION 1 What is the most significant Variabel of the Land Value system? 2 What is the most proper model of the Land Value system? 3 How is the result of MRA method compare with the ANN Method? OBJECTIVES Objective The aim of this study is to develop the automatic land valuation method using spatial analysis and artificial neural network.

12 COMAPRISON OF THE RESULT CONCLUSSION Multiple regression analysis (MRA) is the most widely used method for calibrating model. The used of MRA has been the long standing choice for calibration of land value model. MRA is a statistically based analysis that evaluates linear relationship between a dependent (response) variable and several independent (predictor variable), and extracts parameter estimates for independent variables used collectively to estimate value in a mathematical model. Artificial neural network can calibrate models that consist of both linear and nonlinear term simultaneously.

13 CONCLUSSION LINEARITY ASSUMPTION CANNOT BE SUPPORTED BY THE LAND VALUE VARIABLES THE USE OF NONLINEAR METHOD IS RECOMMENDED FOR THE LAND VALUE MODELING The land value modeling using spatial analysis and artificial neural network is a promising method for the automatic land valuation activities. LOGO

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